Abstract:

An embodiment of the invention is a method of generating a final exposure
setting, including, (a) selecting one of a number of predetermined
exposure settings as a current exposure setting for a solid state camera
having a camera imager, (b) generating a captured scene by the camera
imager using the current exposure setting, (c) selecting according to an
automated search methodology another one of the exposure settings to be
the current setting in response to the captured scene being underexposed
or overexposed, and, (d) repeating (b) and (c) until the captured scene
is neither underexposed or overexposed.

Claims:

1. A method for generating an exposure setting, comprising:a) selecting
one of a plurality of predetermined exposure settings as a current
exposure setting for a camera having a camera imager;b) generating a
captured scene by the camera imager using the current exposure setting;c)
selecting according to an automated search methodology another one of the
exposure settings to be the current setting in response to determining
the captured scene as being underexposed or overexposed; andd) repeating
b) and c) until the captured scene is neither underexposed or
overexposed, wherein the search methodology performs a coarse granularity
search so long as the captured scene is either grossly overexposed or
grossly underexposed, and then changes to a fine granularity search if
the captured scene is still overexposed or underexposed but not grossly
so, wherein the search methodology changes from coarse to fine when a
histogram of the captured scene is determined to indicate that pixels are
spread substantially across a full A/D converter range without
substantial clipping at either extreme of the range.

2. The method of claim 1 wherein the determination of whether the captured
scene is under or overexposed during the fine granularity search
methodology is made by comparing captured pixels, using the current
exposure setting, to a function that is defined to be of a noise variable
representing noise in an imaging data path of the camera imager.

3. The method of claim 2 wherein the noise variable is defined as a noise
function of one or more exposure parameters, the function being
determined by analyzing pixel values in a plurality of dark frame
portions captured by the camera under different exposure settings.

4. The method of claim 3 wherein the noise function is determined upon
camera power-up, before the camera is ready to take pictures, when the
plurality of dark frame portions are captured and histogrammed, an
arithmetic mean is computed for each histogram, and an equation that
relates the noise variable to said exposure parameter variables is fitted
to data points derived from the arithmetic mean computation.

5. The method of claim 1 wherein the plurality of predetermined exposure
settings are arranged in a monotonic sequence.

6. The method of claim 1 wherein each exposure setting is in part defined
by a set of exposure parameters for the camera, the exposure parameters
including a gain applied in the imaging data path.

7. The method of claim 6 wherein the gain is applied to analog pixel
signals prior to their being digitized.

8. A method for generating an exposure setting, comprising:a) selecting
one of a plurality of predetermined exposure settings as a current
exposure setting for a camera having a camera imager;b) generating a
captured scene by the camera imager using the current exposure setting;c)
selecting another one of the exposure settings to be the current setting
in response to determining the captured scene as being underexposed or
overexposed; andd) repeating b) and c) in response to the captured scene
being determined to be underexposed or overexposed, wherein the selecting
is in accordance with a coarse granularity search so long as the captured
scene is either grossly overexposed or grossly underexposed, and then
changes to a fine granularity search if the captured scene is still
overexposed or underexposed but not grossly so, wherein the coarse
granularity search is performed, so long as a histogram of the captured
scene is determined to be not centered in, wherein the histogram is
centered in when its pixels are spread substantially across a full A/D
converter range without substantial clipping at either extreme of the
range.

9. The method of claim 8 wherein the determination of whether the captured
scene is under or overexposed during the fine granularity search is made
by comparing captured pixels, using the current exposure setting, to a
function that is defined to be of a noise variable representing noise in
an imaging data path of the camera imager.

10. The method of claim 9 wherein the noise variable is defined as a noise
function of one or more exposure parameters, the function being
determined by analyzing pixel values in a plurality of dark frame
portions captured by the camera under different exposure settings.

11. The method of claim 10 wherein the noise function is determined upon
camera power-up, before the camera is ready to take pictures, when the
plurality of dark frame portions are captured and histogrammed, an
arithmetic mean is computed for each histogram, and an equation that
relates the noise variable to said exposure parameter variables is fitted
to data points derived from the arithmetic mean computation.

12. The method of claim 8 wherein each exposure setting is in part defined
by a set of exposure parameters for the camera, the exposure parameters
including a gain applied in the imaging data path.

Description:

RELATED MATTERS

[0001]This application is a continuation of Ser. No. 12/146,218, filed on
Jun. 25, 2008, entitled "Determining a Final Exposure Setting
Automatically for a Solid State Camera Without a Separate Light Metering
Circuit", which is a continuation of Ser. No. 10/304,838, filed on Nov.
25, 2002, now U.S. Pat. No. 7,403,222, which is a continuation
application of Ser. No. 09/294,851, filed on Apr. 20, 1999, now U.S. Pat.
No. 6,486,915.

FIELD OF THE INVENTION

[0002]This invention is generally related to solid state cameras, and more
particularly to techniques for determining exposure parameters in such
cameras.

BACKGROUND

[0003]Solid state cameras, just like the conventional film camera, are
limited in their ability to take pictures which faithfully replicate the
full range of colors and brightness in a scene. This is because natural
scenes exhibit a wide dynamic range, i.e., some regions of a scene are
very bright while others are very dark. As a result, conventional solid
state cameras, and particularly consumer products such as digital cameras
and video cameras, have a number of adjustable exposure parameters that
control the sensitivity of a camera imager. The best pictures are usually
taken after the camera's exposure parameters have been adjusted according
to the amount of light in the scene. For instance, if the scene is
relatively bright, then the exposure, e.g., the period of time the camera
imager is allowed to "sense" the incident light, is accordingly reduced
so as to better capture the brightness variations in the scene. In
conventional solid state cameras, a separate light meter sensor and
associated circuitry are used to quickly give an immediate luminance
reading of the scene prior to adjusting the exposure parameters and then
taking the picture. However, both the light metering circuitry and the
camera imager must be calibrated, at the time of manufacturing the
camera, to a reference light source. Otherwise, the technique may not
yield the proper exposure parameters.

[0004]There is a limited conventional technique for determining the
optimal exposure that does not use a separate light metering circuit. In
that technique, the camera is equipped with a means for providing a
histogram of the captured scene at a given exposure setting. The
histogram shows a distribution of pixel values obtained by the imager at
the selected exposure setting. A person can then manually change the
exposure setting and then visually evaluate another histogram of the
scene using the new exposure setting. The exposure setting is repeatedly
adjusted in this way until the optimal distribution of pixels has been
obtained, and then the picture is taken using this optimal exposure
setting. This technique suffers, however, when implemented in commercial
solid state cameras, because it is too slow and is not automatic for the
average consumer who likes the point and shoot convenience of automatic
cameras.

SUMMARY

[0005]According to an embodiment of the invention, a method is disclosed
for automatically generating a final set of exposure parameters for a
solid state camera having a camera imager, without using a light metering
circuit separate from the camera imager. An iterative automated search
methodology is used to arrive at the final set of exposure parameters
from an initial exposure setting, and sample captures of the scene are
evaluated at each trial exposure setting.

[0006]In a particular embodiment, the method of generating the final
exposure setting includes selecting one of a number of predetermined
exposure settings as a current exposure setting for the solid state
camera. A captured scene is then generated by the camera imager using the
current exposure setting. In response to the captured scene being
underexposed or overexposed, another one of the exposure settings is
selected to be the current setting according to the automated search
methodology. The two latter steps are repeated until the captured scene
is neither underexposed or overexposed. The search methodology performs a
coarse granularity search so long as the captured scene is either grossly
overexposed or grossly underexposed, and a fine granularity search
otherwise.

[0007]Other features and advantages of the invention will be apparent from
the accompanying drawings and from the detailed description that follows
below.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008]The invention is illustrated by way of example and not by way of
limitation in the figures of the accompanying drawings in which like
references indicate similar elements and in which:

[0009]FIG. 1 illustrates a block diagram of an imaging apparatus according
to an embodiment of the invention.

[0010]FIG. 2 illustrates a flow chart for determining the optimal exposure
setting according to an embodiment of the invention.

[0011]FIG. 3 depicts a table of predetermined exposure settings and an
application of the binary chop search methodology using this table to
determine the optimal setting.

[0012]FIG. 4 shows a histogram of pixel values in a captured scene that is
centered out.

[0015]FIG. 7 shows a pixel "bucket" with relative amounts of noise and
signal being identified.

[0016]FIG. 8 illustrates exemplary sets of exposure parameters for
determining the relationship between noise and the exposure parameters of
a solid state camera.

[0017]FIG. 9 shows a plot of noise values measured for different
integration times, and a best linear fit to such values.

DETAILED DESCRIPTION

[0018]An embodiment of the invention is directed to a method for
automatically determining an optimal exposure setting prior to taking
each picture using a solid state camera. The method may be used to
determine the proper exposure setting when initiated by a person pressing
a camera's shutter button down at least half-way. The final setting is
selected from a number of previously determined exposure parameters using
a multi-tiered automated search methodology. These parameters may be
general and not specific to any particular camera, or they may be
customized for the particular camera. The individual exposure parameters
can be determined by those of ordinary skill in the art by conventional
exposure calculations. Several tests are given here to evaluate the
effectiveness of each selected exposure setting, based on a statistical
analysis of a sample capture of the scene using that setting. A technique
is given that computes the expected noise level for various exposure
settings, based on relationships between noise and exposure parameters
determined at each power-up of the camera. Use of this expected noise
level helps achieve speedy and accurate determination of the optimal
exposure.

[0019]In these embodiments, the invention presents a further advantage in
that it reduces manufacturing cost of the camera by eliminating a light
metering circuit separate from the camera imager. Also, use of the camera
imager for both capturing the final picture and determining the optimal
exposure eliminates calibration steps that would normally be required to
characterize the light metering circuit and the camera imager at the time
of manufacturing the camera. This is in part because the sample captured
scenes used to determine the exposure setting are obtained through the
same imaging data path for the final picture. Thus, the signal level in
the final picture will be the same as the signal level in the sample
captured scene and is, therefore, properly accounted for when determining
the optimal exposure setting. An additional advantage of certain
embodiments of the invention is that they enable the signal to noise
ratio of the final picture to be optimized for ambient temperature
conditions, so that the full capability of the camera is realized.

[0020]FIG. 1 illustrates an imaging apparatus 100 according to one or more
embodiments of the invention. The apparatus 100 includes optics 104 that
has a conventional aperture, filter, and lens system used to guide the
incident light into the camera and onto an imager 108. The imager 108
includes a number of photocells 112 normally arranged as a sensor array
and located at a focal plane of the optics 104. Each photocell 112
normally outputs an analog pixel intensity value. These pixel values may
then be subjected to analog processing 116 before being forwarded to an
analog-to-digital (A/D) converter 120. The analog pixel values are then
digitized by the A/D converter 120 and may be subjected to digital
processing 124 before being provided as a video data stream, or as still
images for storage in electronic image file format. These constituent
components of the imager 108 may, of course, be implemented in a variety
of different ways. For instance, the photocells 112 and the analog
processing 116 may be part of the same integrated circuit die. If allowed
by the die manufacturing process, the A/D converter 120 and the digital
processing 124 may also be integrated onto the same die. This may be
particularly desirable if the entire imager 108 is implemented using a
complimentary metal oxide semiconductor (CMOS) fabrication process.
Alternatively, the digital processing 124 may be implemented separately
from the photocells 112 where, for instance, the photocells 112 are based
on charge coupled device (CCD) technology. In general, the exposure
control techniques described here may be implemented using a wide range
of technologies for the imager 108.

[0021]The imager 108 and the optics 104 are under the control of automatic
exposure control block 128. The automatic exposure control block 128
evaluates digitized pixel values for one or more sample captured scenes,
and in response determines the appropriate exposure setting. Each
exposure setting is defined by a number of exposure parameters. These
include aperture size for the optics 104, one or more integration times
applied to the photocells 112, a gain value (normally an analog gain
value provided to the analog processing 116), and a flash signal to a
strobe 132 used to further illuminate the scene. The exposure setting may
be a combination of one or more of these parameters and perhaps
additional parameters that may need to be controlled to provide the best
quality picture, as recognized by one of ordinary skill in the art.

[0022]The integration time defines the amount of time that a photocell 112
is allowed to detect incident light. Depending on the particular
technology used for the photocell, the integration time may be
implemented in various different ways. For instance, in a photocell 112
implemented as part of a CMOS active pixel sensor, the integration time
is the interval between the moment at which a photodiode is isolated,
such that its voltage is allowed to decay in response to the incident
light, and the point at which the photodiode voltage is read by external
circuitry.

[0023]The gain parameter sets the analog voltage and/or current gain to be
applied to the pixel values prior to their being digitized by the A/D
converter 120. In addition or as an alternative to analog gain, a digital
gain applied by the digital processing 124 may be controlled by the gain
parameter.

[0024]The aperture parameter controls the amount of incident light that
enters the optics 104. A wide range of different automated aperture
mechanisms may be used to provide the desired range of F-stops.
Alternatively, the aperture may be fixed as in certain low-cost consumer
cameras.

[0025]The analog processing 116 may include correlated double sampling
circuitry, as well as any gain and filtering needed to translate the
analog pixel values into the proper input values required by the A/D
converter 120. The output range of the A/D converter 120 is typically
fixed, such as 0-255 for an 8-bit converter, and the entire range of
analog pixel values are mapped into digitized pixel values in this range.
The digital processing 124 may be used to format the digitized pixel
values into a form accepted by the automatic exposure control block 128.
Normally, the exposure control techniques of the invention, and in
particular those in which an expected noise value is computed, are
applied to raw pixel values, which are not yet subjected to dark current
noise reduction or any image processing algorithms. However, it is also
possible to use pixel data that has been converted to luminance or other
color filter array interpolated formats.

[0026]The automatic exposure control block 128 may be implemented as
processor instructions on a machine-readable medium such as a
semiconductor memory, as dedicated hardwired logic for greater speed of
execution, or as a combination of the two. In a particular embodiment of
the invention, the imager and the optics form an electronic camera, such
as a digital camera, while the exposure control block 128 is implemented
by software loaded into a separate data processing device which is not
exclusively a stand-alone camera, such as a personal computer.
Alternatively, the exposure control block may be integrated in the
electronic camera.

[0027]A particular methodology to be followed by the exposure control
block 128 is shown as a flow chart in FIG. 2. FIG. 2 illustrates an
embodiment of the invention that provides an approach to determining the
optimal exposure, in conjunction with FIG. 3. FIG. 3 shows a table-based
coarse and fine granularity search strategy. In this embodiment, a lookup
table contains a number of predetermined exposure parameters for each
exposure setting, where each exposure setting may correspond to a given
illumination level. In this example, there are 50 exposure settings that
have been predetermined and are arranged with decreasing exposure as
shown. Each exposure setting may have an index value, a gain value, an
integration time, and an aperture size. Operation begins here with step
301 in FIG. 2 when the camera is powered up. In step 302, the noise in
the captured raw pixel values is characterized as a function of
integration time (Lint) and gain in the imaging data path. Techniques for
determining this noise will be described below. For now, it is sufficient
to recognize that this noise will be used to set an expected value and an
exposure aim value for subsequent captures, prior to analyzing each
capture for underexposure or overexposure.

[0028]When the user has aimed the camera at the desired scene and starts
to depress the shutter button in step 303, operation continues with step
304 in which a sample window of pixels having a camera system-defined
position and size is defined. The sample window will initially encompass
all of the scene. Operation then continues with step 308 in which the
initial capture is made with exposure parameters associated with a
default exposure setting/illumination level. The image from this captured
sample window is then histogrammed. Operation continues thereafter with
any one of steps 312, 320 or 336. For this example only, operation
continues with step 312, in which the test is whether the histogram data
is "centered out." An example of what is meant by centered out is shown
in FIG. 4, where the pixel values are spread across the full range but
are "clipped" at the minimum (noise floor) or maximum (2N, where
N is the number of bits provided by the A/D output). Clipping occurs
when, for instance, 5% of the total number of pixels in the sample window
have the maximum or minimum value. Test 312 will also fail if the sample
window is already at the minimum size allowed.

[0029]If the test in step 312 is true, then this means that the imager's
dynamic range is far too small to capture the whole scene's dynamic
range. Thus, the current sample window may not be the best window to
determine the optimal exposure setting for this particular scene. In this
case, operation will proceed with step 316 in which the sample window is
reduced to concentrate effort on determining a final exposure for the
main subject, which is likely positioned in the center of the scene. This
change may be beneficial in that it might exclude certain peripheral
elements in the scene which may be less important to render with detail,
such as, for instance, the sun in the sky. For instance, making the
sample window smaller and centered over the captured scene is generally
appropriate for most consumer applications, as consumers tend to center
the subject of interest when taking a picture. The reduction in window
size is allowed until the size reaches a predetermined minimum. After
changing the window, operation then loops back to step 308 where a
histogram is performed of the new sample window and the test in step 312
is repeated.

[0030]If the test in step 312 is not true, then the next test may be step
320 to determine whether the histogram data is "centered in," as shown in
FIG. 5. The term centered in may loosely describe a histogram in which
the pixels are spread across a significant portion of the full A/D
converter range and exhibit no significant clipping, if any, at either
extreme. If the histogram is not centered in, then the next exposure
setting will be selected based on an efficient table search strategy with
coarse granularity (see FIG. 3). For example, the binary chop is known to
be a highly efficient coarse granularity search technique. In that case,
operation proceeds with step 324 in which the captured scene, and, in
this particular embodiment, data from just the sample window, is
evaluated to determine whether the current exposure setting yielded a
capture that is underexposed, i.e., too dark. An underexposed scene means
that the histogram will show few, if any, mid or light tones. This may
occur, for instance, if the maximum value in the histogram is less than
an aim mean value for the histogram. If so, then the next exposure should
be greater, i.e., longer integration time, increased gain and/or aperture
size. If underexposed, then operation proceeds with step 328 in which the
search algorithm is applied to select a greater exposure setting.

[0031]Returning to step 324, if the capture has few, if any, mid or dark
tones, such as when the minimum histogram value is greater than the aim
mean, then the image is overexposed. In a particular embodiment of the
invention, this aim mean is 18% of the maximum digitized signal range,
i.e., noise value+(2N-1-noise value)*0.18. This is based on the
assumption that the optimal exposure setting for the scene is the same as
that needed to properly expose an equivalent 18% gray card, under the
same lighting as the scene being captured. If overexposed, then operation
proceeds with step 332, such as by performing a binary chop of the
current index range (between top and bottom) as in table 404a, and then
resetting the range, as shown in table 404b (FIG. 3).

[0032]Returning now to decision step 320, if the centered in test is true,
then the current exposure setting resulted in a capture that is neither
grossly overexposed or grossly underexposed. In addition, if preceded by
test 312 for "centered out," this will also mean that any clipping at the
outer limits of the histogram is either not an issue or has been
addressed to the best of the system's ability. As a result, the search
methodology for the next exposure setting changes from coarse granularity
to fine granularity, beginning with step 336.

[0033]In step 336, the histogram is further tested to determine whether
the histogram mean is within the allowable tolerance of the aim mean. If
this test is true, then the optimal exposure setting has been found, such
that the final picture may be taken using this setting. If, however, the
test in step 336 is not true, then operation continues with step 340 in
which the mean of the current histogram and the mean of a histogram of a
previous capture are compared to the aim mean. If the aim mean is
straddled by the mean values of the current and previous captures, then
operation continues with step 344 in which the exposure setting that
yielded the histogram mean being closest to the aim mean is chosen to be
the optimal setting. The straddling of the aim mean may occur if the
captured scene, and in particular the sample window, has exceedingly high
contrast. The tolerance around an 18% aim mean value may be selected by
one of ordinary skill in the art following an error budget analysis to
determine the best exposure tolerance bands. For digital cameras having
imagers built using metal oxide semiconductor (MOS) fabrication processes
and 8 bit digitized pixel values, an aiming for less than 1/4 exposure
value (EV) error and given a black level (noise floor) of 54 A/D units,
an aim mean of 90 units may be selected with a tolerance of +/-6 A/D
units.

[0034]If the outcome of the straddle test in step 340 is false, then the
histogram mean using the current exposure setting is compared to the aim
mean of the histogram. If the histogram mean is greater than the aim
mean, i.e., overexposed, then the exposure setting index is incremented
to the next adjacent (higher) inferred illumination level and its
corresponding exposure setting. If the histogram mean is less than the
aim mean, i.e., underexposed, then the index is decremented to the next
adjacent (lower) inferred illumination level and its corresponding
exposure setting (see FIG. 3, table 404c). Operation then loops back to
step 308 where another capture of the scene is made using the new
exposure setting.

[0035]FIG. 3 shows the use of coarse and fine granularity searches in a
list of exposure settings to determine the optimal exposure. In this
embodiment, a lookup table is created that contains a number of exposure
parameters for each exposure setting. In this example, there are 50
exposure settings that have been predetermined and are arranged in
decreasing exposure as shown. Each setting may be defined by a gain
value, an integration time, and an aperture size. Other exposure
attributes, such as flash use, may also be included. Operation will begin
with selecting a current exposure setting at the index position 10. The
camera will then obtain the gain, integration time and aperture size
associated with index 10 in the lookup table. A capture of the scene
using this current exposure setting will then be executed and evaluated.
Rather than selecting the initial exposure setting as the halfway point
between the top and bottom of the table, the initial setting is selected
slightly closer to the top, because the integration times in the upper
half of the table are longer than those in the lower half of the table,
so that a completed search using an initial exposure setting from near
the top of the table will consume a more uniform amount of time for any
possible illumination level.

[0036]Thus, with the current setting at index 10 yielding an overexposed
scene, a binary chop is performed to select the next setting at index 30,
which is halfway between a current setting and the bottom of the table.
The scene is then captured again using the new current setting at index
30. Note that the top boundary of the range of exposure settings in Table
404(b) is now at index 10. If the current exposure setting at index 30
results in an underexposed scene, then the binary chop will pick the
point halfway between index 10 and index 30, which is index 20. In Table
404(c), the current setting is now at index 20. Assume now that the
current setting at index 20 results in a capture of the scene having a
centered in characteristic, as determined by its histogram. This means
that the captured scene is only mildly over- or underexposed, such that
the binary chop algorithm should be abandoned in favor of an incremental
step to the next adjacent exposure setting. Thus, if the histogram data
indicates that the current captured scene is still underexposed, then the
exposure setting index is decremented from 20 to 19, as shown by the
pointers in Table 404(d). Once again, if the captured scene using the
current setting at index 19 is still underexposed, then the setting index
is decremented to 18. Finally, if the histogram mean of the captured
scene obtained using the current setting at index 18 is within the
tolerances surrounding the aim mean, then the optimal exposure setting is
found, as indicated in Table 404(e).

[0037]The inventors have discovered that it is useful to switch from
coarse granularity to fine granularity, while searching for the final
exposure among a number of predetermined exposure settings, when the
current capture of the scene becomes under- or overexposed at a
relatively mild level. Otherwise, continued use of coarse granularity for
selecting the next exposure setting may not converge to the final
setting. The failure of a course granularity search such as the binary
chop may occur because the mean of the pixel values in each capture
shifts with different exposure levels. When the scene dynamic range
exceeds what a camera may capture, pixels will be clipped at the maximum
and minimum A/D boundaries. As different exposures change the amount of
incoming light, some clipped pixels at one A/D extreme will no longer be
clipped. This, in turn, affects the histogram's mean value which is an
input parameter of the binary chop technique. Because this input
parameter changes in an inexact, unpredictable manner with different
exposures, the binary chop will fail to converge for some scenes.

[0038]The embodiments of the invention described above utilize the concept
of an aim mean that is compared to a histogram mean to help determine
when the search methodology should switch from coarse granularity to fine
granularity and also when the captured scene is over or underexposed. In
a particular embodiment of the invention, the aim mean is replaced with a
"dynamic" aim mean that is computed as a function of each exposure
setting. A dynamic aim mean as defined here is a noise-dependent variable
that is computed for each captured scene, based on the current exposure
setting. The possible sources of noise that may be taken into account
when determining the dynamic aim mean are illustrated in FIG. 7. This
figure shows a pixel "bucket" showing the relative amounts of the
different types of noise that are captured in a conventional digital
camera. These types contribute to the aforementioned noise floor and
should be considered when determining a formula for the noise floor. In a
particular embodiment of the invention, the dynamic aim mean is defined
as follows:

where the Dynamic Mean Noise is defined as the expected mean of the noise
floor (a function of integration time and gain, see below). In the
example given in this disclosure, N=8 bits.

[0039]The inventors have determined that the final exposure setting for
taking a picture using a digital camera may be found relatively quickly
using the above-described automated methodology when the histogram mean
is compared to a dynamic aim mean computed for each given exposure
setting. In a further embodiment of the invention that uses the dynamic
aim mean, mathematical relationships that define a noise variable as a
function of different exposure parameter variables are determined. These
relationships may be linear (y=ax+b, where y is noise and x is the
exposure parameter) or may alternatively be of higher order if needed to
more accurately describe the relationship. They can be determined by
fitting a curve or line to the measured test data, as shown in the
example of FIGS. 8 and 9. FIG. 8 shows a set of exposures that will be
captured with the shutter in the closed condition to assess the camera's
noise level at the current temperature. Note that the series of exposure
parameters in FIG. 8 have an integration time series at a fixed gain and
a gain series at fixed integration time. The time series is used to
derive a general relationship for noise vs. integration time. This can be
done linearly, as illustrated in FIG. 9, or as a higher order regression
as needed. The test data may be gathered from a set of closed shutter
captures (dark frames) obtained at the ambient temperature at which the
final picture is to be taken. The closed shutter captures can be obtained
upon camera power-up as illustrated in FIG. 2 step 302 or at any other
convenient time, normally prior to the user depressing the shutter button
to take a picture. By using such predefined mathematical relationships
between the noise floor and the various exposure parameters, there is no
need to capture and process any dark frames each time a picture of a new,
different scene is taken, thus promoting a speedier determination of the
final exposure. The mathematical relationships for the present
embodiments include noise vs. pixel integration time, N(Tint), and noise
vs. gain in the imaging data path prior to digitization, N(G). For
instance, if the N(Tint) line is described by a linear fit,
N(Tint)=a3*T+b1, and if the N(G) line is described by
N(G)=a5*G+b2, then the dynamic mean noise can be given by:

dynamic mean noise∝a3*T_integration+a5*gain+b5

where b5=b1+b2 and where proportionality constants have
been omitted. By using such predetermined mathematical formulas to
determine the noise floor, the dynamic mean noise, and the dynamic aim
mean as a function of each trial exposure setting, a more accurate
determination of the exposure setting may be obtained. Use of such a
technique also allows the dynamic range of the scene to be mapped onto
the camera's available dynamic range.

[0040]To summarize, various embodiments of the invention as a method for
determining a final exposure setting automatically for a solid state
camera without a separate light metering circuit have been described. In
the foregoing specification, the invention has been described with
reference to specific exemplary embodiments thereof. It will, however, be
evident that various modifications and changes may be made thereto
without departing from the broader spirit and scope of the invention as
set forth in the appended claims. For instance, the exposure
determination techniques described above may be applied to a wide range
of solid state cameras, including video cameras. Also, the invention is
not limited to the centered in and centered out tests described above.
One of ordinary skill in the art after having read this disclosure may be
able to develop alternative tests for determining when a series of
captures change from being grossly overexposed or grossly underexposed to
being only mildly so. The specification and drawings are, accordingly, to
be regarded in an illustrative rather that a restrictive sense.